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Mon 19 Jun 2017 17:00 - 17:25 at Aula Master - Static Analysis Chair(s): Loris D'Antoni

Mainstream points-to analysis techniques for object-oriented languages rely predominantly on the allocation-site abstraction to model heap objects. We present MAHJONG, a novel heap abstraction that is specifically developed to address the needs of an important class of type-dependent clients, such as call graph construction, devirtualization and may-fail casting. By merging equivalent automata representing type-consistent objects that are created by the allocation-site abstraction, MAHJONG enables an allocation-site-based points-to analysis to run significantly faster while achieving nearly the same precision for type-dependent clients. MAHJONG is simple conceptually, efficient, and drops easily on any allocation-site-based points-to analysis. We demonstrate its effectiveness by discussing some insights on why it is a better alternative of the allocation-site abstraction for type-dependent clients and evaluating it extensively on 12 large real-world Java programs with five context-sensitive points-to analyses and three widely used type-dependent clients. MAHJONG is expected to provide significant benefits for many program analyses where call graphs are required.

Mon 19 Jun
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16:10 - 17:50: PLDI Research Papers - Static Analysis at Aula Master
Chair(s): Loris D'AntoniUniversity of Wisconsin–Madison
pldi-2017-papers16:10 - 16:35
Zachary KincaidPrinceton University, Jason BreckUniversity of Wisconsin-Madison, Ashkan Forouhi BoroujeniUniversity of Wisconsin-Madison, Thomas RepsUniversity of Wisconsin - Madison and Grammatech Inc.
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pldi-2017-papers16:35 - 17:00
Rei ThiessenUniversity of Waterloo, Ondřej LhotákUniversity of Waterloo, Canada
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pldi-2017-papers17:00 - 17:25
Tian TanUNSW Australia, Yue LiUNSW Australia, Jingling XueUNSW Australia
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pldi-2017-papers17:25 - 17:50
Anirudh SanthiarIndian Institute of Science, Aditya KanadeIndian Institute of Science, Bangalore
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